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Neural networks for whole ion mobility spectra from a standardized data base of 1295 spectra for 195 chemicals at various concentrations showed 92% successful classifications by functional group was throughout a range of concentrations. Application of neural networks in a two tier design where chemicals were first identified by class and as individual substances eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit development of automated identification systems. Under certain conditions of temperature and moisture in the IMS drift tube, the identification of “blind unknowns” was better than 90%. This suggests that the volatile organic analyzer can be extended to completely unknown chemicals during air quality monitoring.

Hydrazine (N2H4) and monomethylhydrazine (MMH) are used as propellants in several space-based applications, in which exposure limits as low as 2 ppb have been proposed. This paper reviews the development of hand-held, ambient-temperature instruments that use ion mobility spectrometry (IMS) in the detection of hydrazine and MMH. An instrument, based on early designs, detected hydrazine at 6 ppb with no interference from vapors except for ammonia, but exhibited slow response and recovery times. Performance of a hand-held IMS instrument that used water-reagent ion chemistry was unacceptable. An alternative, using acetone as the dopant reagent, also proved unacceptable, because ammonia-acetone clusters produced substantial interference in the detection of MMH. The goal of the present development effort was to eliminate ammonia interference through altering the ionization chemistry.